Cat modeling data uncertainty and its impact 01

Data uncertainty and its impact for CAT Modeling
“It’s tough to make predictions, specially about the future” – Yogi Berra.
In this brief piece of work, I would like to show and share with you, a
summary of the findings I have discovered in almost 8 years working as CAT
Modeling Analyst, particularly for Latin American Clients (Mexico, Colombia,
Costa Rica, etc).
These pages will show you why it is important to get the most accurate data
for the risks to be modeled, both for the Modeler, the Client, the Insurance
Company, the Reinsurers, etc.

Data quality it’s crucial in order to perform a reasonably good CAT Modeling
Analysis.
The following examples will help us to visualize the importance of collecting
much and better information about the Risks.
The risk I have chosen is a REAL building and for most of the missing data,
you will only need to invest a bit of time to search for information in the
Internet.

Case Study: Mexico City, Torre Mayor
The Torre Mayor is currently one of the tallest buildings in Mexico City. It was
finished in 2003 and the 55 levels were occupied almost immediately by
large and very important National and International Companies.
Building site is located in one of the areas for which the soil is not really the
best for such a high edification and the effects of seismic resonance and
liquefaction could have a serious impact if the building engineering was not
the best.
The estimated replacement cost for this tower is approximately USD 280
Millions, not considering Contents nor Time Element Coverages.

Tipically, Insurance Companies get the following data in order to quote the
Risk:
GeoCoding: Old CRESTA Zone 11 or AMIS Critical Zone E.
Replacement value: USD 280 Million (only for Buildings Coverage).
Constructive type: reinforced concrete.
Occupancy: General Commercial.
Year of construction: Unknown.
Number of stories: unknown, or possibly the real figure, 55 stories.
Deductible: 5%, but not specific if it is based on the replacement value or the amount of loss.
Policyl limit: either USD 280 Million or even not provided.

After running PML estimation using one of the most famous commercial CAT
Models with the data originally provided, you will get this:
Return Period (years)
Annual Aggregated Loss
(USD)
TVaR
(USD)
50 109,490 1,629,160
100 704,258 2,908,937
200 1,594,434 4,754,204
250 1,904,194 5,505,919
1,000 6,900,288 11,605,322
1,500 7,660,425 14,543,261
5,000 14,294,997 20,414,315
10,000 26,553,633 26,533,633
Annual Aggregated Loss 33,132
Standard Deviation 468,885
Coefficient of Variation 14.1520

What could happen with the previous figures using more accurate data?
After investing about 10 minutes using Google, I found this:
GeoCoding: Latitude 19.4242, Longitude -99.1756 (improved)
Replacement value: USD 280 Million (only for Buildings Coverage).
Constructive type: steel reinforced concrete frame, appliying seismic resistance technology
(improved).
Occupancy: Professional and Consultancy Services (Offices) (improved).
Year of construction: 2003 (year finished) (improved).
Number of stories: 55 stories.
Deductible: 5% of the replacement value.
Policyl limit: either USD 280 Million (Total Loss).
Let’s see next the effect of these changes in the PML estimation…

After running PML estimation using the improvements in data, you will get
this set of results:
(USD)
TVaR
(USD)
50 0 518,264
100 0 1,036,528
200 135 2,073,050
250 1,733 2,591,204
1,000 3,834,701 8,351,572
1,500 5,403,906 10,977,725
5,000 9.823,017 16,868,176
10,000 23,913,335 23,913,335
Annual Aggregated Loss 10,365
Standard Deviation 326,642
Coefficient of Variation 31.5139

Spotting the differences between exercise 1 & 2 the observed changes are:
%
TVaR
%
50 -100 -68.19
100 -100 -64.37
200 -99.99 -56.40
250 -99.91 -52.94
1,000 -44.43 -28.04
1,500 -29.46 -24.52
5,000 -31.28 -17.37
10,000 -9.88 -9.88
Annual Aggregated Loss -69
Standard Deviation -30
Coefficient of Variation 123

Cost and Benefit implications:
•Cost:
•Monetary: practically ZERO
•Time: maximum 10 additional minutes dedicated to search for public data and processing time
(tipically 1 working day or less).
Benefit:
•For the Client: the possibility of getting a cheaper Insurance Cost.
•For the Insurer: the possibility of getting new Clients and demonstrate to Reinsurers to
adequately deal with accurate data for a better Risk Assesment.

Thank you for your interest and time!
Eduardo Sandoval Hernández
esandovalh180869@gmail.com
Cell phone: +52 155 54557789

Cat modeling data uncertainty and its impact 01

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